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Lifestyle- and genetically induced disorders related to disturbances in cholesterol metabolism have shown the detrimental impact of excessive cholesterol levels on a plethora of pathological processes such as inflammation. In this context, two-hydroxypropyl-β-cyclodextrin (CD) is increasingly considered as a novel pharmacological compound to decrease cellular cholesterol levels due to its ability to increase cholesterol solubility. However, recent findings have reported contra-indicating events after the use of CD questioning the clinical applicability of this compound. Given its potential as a therapeutic compound in metabolic inflammatory diseases, in this study, we evaluated the inflammatory effects of CD administration in the context of cholesterol-induced metabolic inflammation in vivo and in vitro. The inflammatory and cholesterol-depleting effects of CD were first investigated in low-density lipoprotein receptor knockout (Ldlr-/) mice that were transplanted with Npc1nih or Npc1wt bone marrow and were fed either regular chow or a high-fat, high-cholesterol (HFC) diet for 12 weeks, thereby creating an extreme model of lysosomal cholesterol-induced metabolic inflammation. In the final three weeks, these mice received daily injections of either control (saline) or CD subcutaneously. Subsequently, the inflammatory properties of CD were investigated in vitro in two macrophage cell lines and in murine bone marrow-derived macrophages (BMDMs). While CD administration improved cholesterol mobilization outside lysosomes in BMDMs, an overall pro-inflammatory profile was observed after CD treatment, evidenced by increased hepatic inflammation in vivo and a strong increase in cytokine release and inflammatory gene expression in vitro in murine BMDMs and macrophages cell lines. Nevertheless, this CD-induced pro-inflammatory profile was time-dependent, as short term exposure to CD did not result in a pro-inflammatory response in BMDM. While CD exerts desired cholesterol-depleting effects, its inflammatory effect is dependent on the exposure time. As such, using CD in the clinic, especially in a metabolic inflammatory context, should be closely monitored as it may lead to undesired, pro-inflammatory side effects.

Gambling is an ordinary pastime for some people, but is associated with addiction and harmful outcomes for others. Evidence of these harms is limited to small-sample, cross-sectional self-reports, such as prevalence surveys. We examine the association between gambling as a proportion of monthly income and 31 financial, social and health outcomes using anonymous data provided by a UK retail bank, aggregated for up to 6.5 million individuals over up to 7 years. Gambling is associated with higher financial distress and lower financial inclusion and planning, and with negative lifestyle, health, well-being and leisure outcomes. Gambling is associated with higher rates of future unemployment and physical disability and, at the highest levels, with substantially increased mortality. Gambling is persistent over time, growing over the sample period, and has higher negative associations among the heaviest gamblers. Our findings inform the debate over the relationship between gambling and life experiences across the population.

We ask to what extent task knowledge and tactics, or enduring personality traits, predict behaviour and biases in a stock trading setting. We base our study on an exceptionally wide-ranging dataset: responses to a self-report survey, together with transactional data of the same individual customers of a UK brokerage. From the survey we estimate measures of domain-general personality traits, such as the Big Five, and task-related measures of financial literacy, competency and attitude specific to trading. Our results show the dominance of task measures over trait measures, when predicting nine different dependent variables of investment performance, biases and portfolio activity. We argue that researchers into financial decisions should consider a rich set of explanatory factors.

The prevalence of digital footprints can allow researchers to study the personalities of millions of individuals with improved ecological validity. We present spending entropy as a candidate personality trait derived as a feature of an objective big data source---mass-transactional data from millions of bank accounts. Entropy measures the unpredictability of spending and acts as a measure of the chaotic nature of a person's life. Over and above how much money people spend, and what the money is spent on, spending entropy positively relates to future financial distress. High entropy leads to increased probability of missed payments across financial products. Entropy temporally relates to future distress three months ahead including more severe measures of distress. We replicate our findings in personal current account, loan, and mortgage holders in a second financial institution. Our findings suggest that high-dimensional data can be used to build psychological traits that predict outcomes in novel situations.

We provide the first tests to distinguish whether individual investors equally balance their overall portfolios (naive portfolio diversification—NPD) or engage in naive buying diversification (NBD)—equally balancing values in same-day purchases of multiple assets. We find NBD in purchases of multiple stocks, and in mixed purchases of individual stocks and funds. In contrast, there is little evidence of NPD. So investors seem to narrowly frame their buy-day decision. Simulation analysis suggests that NBD substantially reduces investor welfare. These findings suggest that behavioral finance theory should incorporate transactional as well as portfolio framing.

In case-based reasoning (CBR), problems are solved by retrieving prior cases and adapting their solutions to fit; learning occurs as new cases are stored. Controlling the growth of the case base is a fundamental problem, and research on case-base maintenance has developed methods for compacting case bases while maintaining system competence, primarily by competence-based deletion strategies assuming static case adaptation knowledge. This paper proposes adaptation-guided case-base maintenance (AGCBM), a case-base maintenance approach exploiting the ability to dynamically generate new adaptation knowledge from cases. In AGCBM, case retention decisions are based both on cases' value as base cases for solving problems and on their value for generating new adaptation rules. he paper illustrates the method for numerical prediction tasks (case-based regression) in which adaptation rules are generated automatically using the case difference heuristic. In comparisons of AGCBM to five alternative methods in four domains, for varying case base densities, AGCBM outperformed the alternatives in all domains, with greatest benefit at high compression.

Publisher Summary This chapter discusses the systems of care outcome profile for the Hawaii Ohana project (HOP). The HOP is based on the system of care guiding values and principles that have been promoted by the Child and Adolescent Service System Program. The three basic guiding values are that services should be (1) child- and family-centered—that is, services should flexibly meet unique individual and family needs rather than trying to fit individuals and families into fixed service categories, (2) community-based, providing a full spectrum of services in the community to reduce the need to place youngsters in hospitals or other high-cost restrictive settings, and (3) culturally competent, by being in tune with and building on local values and strengths. A notable feature of the HOP is that it is structured to maximize family and community involvement in the planning and evaluation of services, thereby helping to ensure that services are in tune with the local culture and truly meet local needs. The project helped to organize and launch two of Hawaii's first local Community Children's Councils that bring together family and community members and agency representatives and provide ongoing clerical support and technical assistance.

Publisher Summary Services for persons with disabilities have taken on a radically new look over the past 25 years as the focus has shifted from segregation to integration within community environments. This revolutionary shift has been driven by a commitment to normalization and associated principles, and has become widely institutionalized, largely as the result of legal mandates. The primary service settings have changed from large institutions and special schools to community programs and regular schools. The new status quo is being challenged through the promotion of different sets of guiding principles and concomitant transformations in services. Proposed new approaches stress the principles of self-determination by persons with disabilities and their families and community involvement through the building of relationships. Making these principles primary changes the focus of social welfare services from direct provision of services by professionals to promotion of supports naturally present in the community. This chapter explores the concepts of paradigms and paradigm shifts in historical context. The way these concepts are currently applied in the disabilities literature is often different from their initial usage in the hard sciences.

Platelet-derived growth factor (PDGF) has been reported to be a potent mitogen for Swiss 3T3 cells made quiescent by growth to saturation density in high serum, and its activity is further potentiated by high levels of insulin, which alone have little effect. We show here that this is not the case for sparse 3T3 cells made quiescent by plating in low serum. Under these conditions, insulin alone is at least as effective as PDGF and frequently more so. Together, the response is no more than additive at best, and in many cases less than additive, the combined effect being no greater than for insulin alone. Instead, we find that optimal mitogenic stimulation requires the additional presence, besides PDGF and insulin, of hydrocortisone, prostaglandin E1 and an unidentified, non-dialysable component contained in serum treated with dithiothreitol (DTT) to inactivate endogenous growth factors. Interestingly, overnight pretreatment of the cells with hydrocortisone alone potentiates the subsequent response to PDGF + insulin, i.e. pretreatment induces a long-term memory that persists after the removal of the hydrocortisone from the medium. In short-term (24h) thymidine incorporation assays, the combination of PDGF, insulin, hydrocortisone, prostaglandin E1 and DTT-serum, is as effective as optimal levels of whole serum, but is unable to sustain longer-term proliferation (measured over 6 days). For this, high- and low-density lipoproteins, fibronectin and, to some extent epidermal growth factor (EGF), are also necessary.

Financial transactions are increasingly used by consumer apps and financial services providers to estimate consumption-based carbon emissions. This approach promises a low-resource, ultra-fast and highly scalable approach to measuring emissions at different levels of potential policy intervention – spanning the national, subnational, local, and individual-level. Despite this potential, there is a lack of research formally exploring the validity of this approach to carbon profiling. Here we address this oversight in two ways. Firstly, by using transactions from more than 100,000 customers of a large retail bank, we quantitatively compare emission estimates with those calculated from a more standard data source used in carbon profiling, the UK household expenditure survey. Secondly, we offer a detailed qualitative comparison of the advantages and disadvantages of transactions versus alternatives (such as survey data), across dimensions including data availability, data quality and data detail. We find financial transactions offer a credible alternative to survey-based sources and, if made more widely accessible, provide important advantages for profiling emissions. These include objective, micro-level data on consumption behaviours, larger sample sizes and longitudinal, frequent data capture.

Part 5: Automatic Reasoning; International audience; This paper presents the formalization of the matrix inversion based on the adjugate matrix in the HOL4 system. It is very complex and difficult to formalize the adjugate matrix, which is composed of matrix cofactors. Because HOL4 is based on a simple type theory, it is difficult to formally express the sub-matrices and cofactors of an n-by-n matrix. In this paper, special n-by-n matrices are constructed to replace the (n − 1)-by-(n − 1) sub-matrices, in order to compute the cofactors, thereby, making it possible to formally construct aadjugate matrices. The Laplace’s formula is proven and the matrix inversion based on the adjugate matrix is then inferred in HOL4. The paper also presents formal proofs of properties of the invertible matrix.

Part 4: Multi-agent Systems; International audience; In view of the deployment environment of the adaptive system is complex, dynamic, unpredictable, focusing on the construction of dynamic, uncertain environment adaptive system, and this paper combines the reinforcement learning technology and software agent technology to propose an adaptive mechanism based on shared learning in multiple agent system. Based on this, framework for constructing adaptive systems and shared learning algorithm of agent are given. Finally, by conducting a comparative experiment and result analysis to verify the feasibility of the theory put forward by this article.

Part 1: Keynote Presentations; International audience; The very recent development of Cyber-Physical Systems (CPS) provides a smart infrastructure connecting abstract computational artifacts with the physical world. The solution to CPS must transcend the boundary between the cyber world and the physical world by providing integrated models addressing issues from both worlds simultaneously. This needs new theories, conceptual frameworks and engineering practice. In this paper, we set out the key requirements that must be met by CPS systems, review and evaluate the progress that has been made in the development of theory, conceptual frameworks and practical applications. We then discuss the need for semantics and a proposed approach for addressing this. Grand challenges to informatics posed by CPS are raised in the paper.

Part 7: Knowledge Representation; International audience; The indiscernibility relation is the basic concept in Rough set theory, a novel representation of indiscernibility relation using Zero-Suppressed BDDs is proposed in this paper. Through introducing the indiscernibility matrix and the indiscernibility graph, we put forward the encoding of the variable and give the characteristic function. Once the characteristic function is constructed, it can be represented using ZBDDs.And further, combined with an example, we analyze the effectiveness of this method. It provides a basis for deal with rough set computing.

Part 9: Trust Software; International audience; As underlying infrastructure of cloud computing platform, datacenter is seriously underutilized, however, its operating costs is high. In this paper, we implement virtual machines placement algorithm in CloudSim using constraint programming approach. We first formulate the problem of virtual machines placement in virtualized datacenters as a variant of multi-dimensions bin packing problem, and then exploit constraint solver to solve this problem with the objective of minimizing number of physical machines that host virtual machines. Finally, we compare different virtual placement algorithms for evaluating constraint programming-based virtual machine placement algorithm including the built-in virtual machine placement algorithm in CloudSim and FFD algorithm. The experimental results show that constraint programming-based virtual machines placement algorithm can efficiently reduce the number of physical machines to achieve the goal of reducing datacenter operating costs and improving resource utilization.

Part 3: Data Mining; International audience; As there are huge gaps between the local micro interactions among agents and the global macro emergence of self-organizing system, it is a great challenge to design self-organizing mechanism and develop self-organizing multi-agent system to obtain expected emergence. Policy-based self-organization approach is helpful to deal with the issue, in which policy is the abstraction of self-organizing mechanism and acts as the bridge between the local micro interactions and global macro emergence. This paper focuses on how to develop software agents in policy-based self-organizing multi-agent system and proposes a BDIP architecture of software agent. In our approach, policy is an internal component that encapsulates the self-organizing information and integrates with BDI components. BDIP agent decides its behaviors by complying with the policies and respecting BDI specifications. An implementation model and the running mechanism as well as corresponding decision algorithms for BDIP agents are studied. A case of self-organizing system is studied to illustrate our proposed approach and show its effectiveness.

Part 3: Data Mining; International audience; The common belief is that using Reinforcement Learning methods (RL) with bootstrapping gives better results than without. However, inclusion of bootstrapping increases the complexity of the RL implementation and requires significant effort. This study investigates whether inclusion of bootstrapping is worth the effort when applying RL to inventory problems. Specifically, we investigate bootstrapping of the temporal difference learning method by using eligibility trace. In addition, we develop a new bootstrapping extension to the Residual Gradient method to supplement our investigation. The results show questionable benefit of bootstrapping when applied to inventory problems. Significance tests could not confirm that bootstrapping had statistically significantly reduced costs of inventory controlled by a RL agent. Our empirical results are based on a variety of problem settings, including demand correlations, demand variances, and cost structures.

Part 2: Machine Learning; International audience; Traditional learning techniques have the assumption that training and test data are drawn from the same data distribution, and thus they are not suitable for dealing with the situation where new unlabeled data are obtained from fast evolving, related but different information sources. This leads to the cross-domain learning problem which targets on adapting the knowledge learned from one or more source domains to target domains. Transfer learning has made a great progress, and a lot of approaches and algorithms are presented. But negative transfer learning will cause trouble in the problem solving, which is difficult to avoid. In this paper we have proposed an introspective reasoner to overcome the negative transfer learning.Introspective learning exploits explicit representations of its own organization and desired behavior to determine when, what, and how to learn in order to improve its own reasoning. According to the transfer learning process we will present the architecture of introspective reasoner for transductive transfer learning.

Part 10: Internet of Things; International audience; Wireless sensor network is one of the fundamental components of the Internet of Things. With the growing use of wireless sensor networks in commercial and military, data security is a critical problem in these applications. Considerable security works have been studied. However, the majority of these works based on the scenarios that the sensitivities of data in the networks are in the same. In this paper, we present a cluster-based multilevel security model that enforces information flow from low security level to high security level. The design of the model is motivated by the observation that sensor nodes in numerous applications have different security clearances. In these scenarios, it is not enough for just protecting the data at a single level. The multilevel security mechanism is needed to prevent the information flow from high level nodes to low level nodes. We give the formal description of the model and present a scheme to achieve it. In our model, sensor nodes are grouped into different clusters. In each cluster, the security clearance of sensor nodes must not be higher than the security clearance of the cluster head. We use cryptography techniques to enforce the information flow policy of this model. The higher level nodes can derive the keys of lower level nodes and use the derived key to get the information from lower-level nodes. abstract environment.

Part 1: Keynote Presentations; International audience; In this talk I will first briefly summarize the many impressive results we have achieved along the road so far traveled in the field of AI including some concrete results obtained at the IIIA-CSIC. Next I will describe some of the future challenges to be faced along the (long) road we still have ahead of us with an emphasis on integrated systems, a necessary step towards human-level AI. Finally I will comment on the importance of interdisciplinary research to build such integrated systems (for instance, sophisticated robots having artificial cartilages, artificial muscles, artificial skin, etc) using some examples related to materials science.

Part 3: Data Mining; International audience; Image classification is a challenging problem in computer vision. Its performance heavily depends on image features extracted and classifiers to be constructed. In this paper, we present a new support vector machine with mixture of kernels (SVM-MK) for image classification. On the one hand, the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible. SVM-MK, on the other hand, is constructed to shoot for better classification performance. Experimental results on the Berg dataset show that the proposed image feature representation method together with the constructed image classifier, SVMMK, can achieve higher classification accuracy than conventional SVM with any single kernels as well as compare favorably with several state-of-the-art approaches.

Part 6: Information Retrieval; International audience; Popular web search engines tend to improve the relevance of their result pages, but the search is still keyword–oriented and far from ”understanding” the queries’ meaning. In the article we propose an interactive question-based search algorithm that might come up helpful for identifying users’ intents. We describe the algorithm implemented in a form of a questions game. The stress is put mainly on the most critical aspect of this algorithm – the selection of questions. The solution is compared to broadly used decision trees learning algorithms in terms of knowledge inconsistencies tolerance, efficiency and complexity.

International audience; This overview of different applications of CBR in petroleum engineering is based on a survey and comparative evaluation of different successful applications of CBR. The number of papers and research groups is indicative of importance, need, and growth of CBR in different industries. Application-oriented research in the area of case based reasoning has moved mature research results into practical applications. In this paper we present the evolving story of CBR applied in petroleum engineering especially in drilling engineering. Drilling engineering contains several potential domains of interest, in which CBR can be employed successfully.

International audience; In this paper we discuss different architectures for reasoning under uncertainty related to our ongoing research into building a medical decision support system. The uncertainty in the medical domain can be divided into a well understood part and a less understood part. This motivates the use of a hybrid decision support system, and in particular, we argue that a Bayesian network should be used for those parts of the domain that are well understood and can be explicitly modeled, whereas a case-based reasoning system should be employed to reason in parts of the domain where no such model is available. Four architectures that combine Bayesian networks and case-based reasoning are proposed, and our working hypothesis is that these hybrid systems each will perform better than either framework will do on its own.

International audience; Services computing is an interdisciplinary subject that devotes to bridging the gap between business services and IT services. It is recognized that Requirements Engineering is fundamental in implementing the service oriented architecture. It takes traditional RE techniques great efforts to model business requirements and search for the appropriate services. In this paper, we propose an ontological approach to facilitate the service-oriented modeling framework. The general idea is to establishing a common semantic language to describe both the business requirements and services capabilities based on their effects on the environment. After that, we used a case study to illustrate this method and showed that substantial efforts can be spared to construct a service model from business requirements.

International audience; Curtin University has developed a multimedia prototype system for use by speech therapists to assist in the rehabilitation of motor speech impaired patients. These are often stroke victims who have to relearn the ability to communicate effectively. The process is usually laborious and instead of the usual card-based prompts used by the therapist, this multimedia solution offers screen and audio prompts together with high quality digitised speech. The result is a reduced work load for the therapist. In parallel with this work, is a Mobility Aid Assistive Device Program for the visually impaired. The support clusters for all these groups, whether they be therapists, caregivers, manufacturers of hardware or software do not interact to the extent they might. The author proposes a collaborative digital ecosystem framework that would assist this challenge.

International audience; Knowledge granularity, usually identified with the size of knowledge granules, seems to be real challenge for knowledge consumers as well as for knowledge creators. In this paper, relationships between knowledge granularity as a result of different ways of a knowledge representation are considered. The paper deals with the problem of developing knowledge grid in the context of encapsulation of knowledge including different dimensions and measures. The origin of the problem is discussed in the first section stressing flexibility of knowledge interpretations. Concepts of knowledge granularity (also from formal point of view) are presented in the next section. The nature of well represented knowledge is considered in the next chapter with references to granularity of knowledge. In the last part of the paper the question of formulating knowledge granularity in the context of knowledge grid concepts is discussed. This document comprising guidelines for authors is divided into several sections.

International audience; To prevent pornography from spreading on the Internet effectively, we propose a novel method of adult image detection which combines bag-of-visual-words (BoVW) based on region of interest (ROI) and color moments (CM). The goal of BoVW is to automatically mine the local patterns of adult contents, called visual words. The usual BoVW method clusters visual words from the patches in the whole image and adopts the weighting schemes of hard assignment. However, there are many background noises in the whole image and soft-weighting scheme is better than hard assignment. Therefore, we propose the method of BoVW based on ROI, which includes two perspectives. Firstly, we propose to create visual words in ROI for adult image detection. The representative power of visual words can be improved because the patches in ROI are more indicative to adult contents than those in the whole image. Secondly, soft-weighting scheme is adopted to detect adult images. Moreover, CM is selected by evaluating some commonly-used global features to be combined with BoVW based on ROI. The experiments and the comparison with the state-of-the-art methods show that our method is able to remarkably improve the performance of adult image detection.

International audience; The present work reports our attempt in developing an English-Arabic bi-directional Machine Translation (MT) tool in the agriculture domain. It aims to achieve automated translation of expert systems. In particular, we describe the translation of knowledge base, including, prompts, responses, explanation text, and advices. In the central laboratory for agricultural expert systems, this tool is found to be essential in developing bi-directional (English-Arabic) expert systems because both English and Arabic versions are needed for development, deployment, and usage purpose. The tool follows the rule-based transfer MT approach. A major design goal of this tool is that it can be used as a stand-alone tool and can be very well integrated with a general (English-Arabic) MT system for Arabic scientific text. The paper also discusses our experience with the developed MT system and reports on results of its application on real agricultural expert systems.

International audience; A new image representation based on distribution of local invariant features to be used in a discriminative approach to image categorization is presented. The representation which is called Probability Signature (PS) is combined with character of two distribution models Probability Density Function and standard signatures. The PS representation retains high discriminative power of PDF model, and is suited for measuring dissimilarity of images with Earth Mover's Distance (EMD), which allows for partial matches of compared distributions. It is evaluated on whole-image classification tasks from the scene and category image datasets. The comparative experiments show that the proposed algorithm has inspiring performance.

International audience; Context is of crucial importance for research and applications in many disciplines, as evidenced by many workshops, symposia, seminars, and conferences on specific aspects of context. The International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT), the oldest conference series focusing on context, provides a unique interdisciplinary emphasis, bringing together participants from a wide range of disciplines, including artificial intelligence, cognitive science, computer science, linguistics, organizational science, philosophy, psychology, ubiquitous computing, and application areas such as medicine and law, to discuss and report on context-related research and projects.This proceedings contains the accepted Brevia and Demonstration submissions.Special thanks are due to Isabel Urdapilleta for her preparation of these proceedings and to Jean-Marc Meunier for his assistance in making sure that the technical needs of the demonstrations were met. The contributions reported here will illustrate the rich range of current contributions to context, and will provide a foundation for drawing on the best work from many areas in the next generation of research on context.; Le contexte représente une importance crucial dans beaucoup de disciplines, comme le nombre de manifestations scientifiques le montrent. La Conférence Internationale et Interdisciplinaire sur la Modélisation et l'Utilisation du Contexte (CONTEXT) est la plus ancienne manifestation scientifique qui permet à des chercheurs d'horizons différents de discuter et présenter leurs résultats de leurs recherches et projets en lien avec le contexte.Les actes présents contiennent les Brevia et Démonstrations qui ont été retenus après examen par le Comité Scientifique.Nous souhaitons remercier tout particulièrement Isabel Urdapilleta pour sa préparation soigneuse des Actes et Jean-Marc Meunier pour la prise en charge des aspects techniques de la session Démonstrations. Ces actes montre le large spectre de contributions possible à l'étude du contexte, et fournissent des bases indispensables pour une amélioration notable des recherches dans beaucoup de domaines pour les prochaines générations de recherches sur le contexte.

International audience; Context is of crucial importance for research and applications in many disciplines, as evidenced by many workshops, symposia, seminars, and conferences on specific aspects of context. The International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT), the oldest conference series focusing on context, provides a unique interdisciplinary emphasis, bringing together participants from a wide range of disciplines, including artificial intelligence, cognitive science, computer science, linguistics, organizational science, philosophy, psychology, ubiquitous computing, and application areas such as medicine and law, to discuss and report on context-related research and projects.This proceedings contains the accepted Brevia and Demonstration submissions.Special thanks are due to Isabel Urdapilleta for her preparation of these proceedings and to Jean-Marc Meunier for his assistance in making sure that the technical needs of the demonstrations were met. The contributions reported here will illustrate the rich range of current contributions to context, and will provide a foundation for drawing on the best work from many areas in the next generation of research on context.; Le contexte représente une importance crucial dans beaucoup de disciplines, comme le nombre de manifestations scientifiques le montrent. La Conférence Internationale et Interdisciplinaire sur la Modélisation et l'Utilisation du Contexte (CONTEXT) est la plus ancienne manifestation scientifique qui permet à des chercheurs d'horizons différents de discuter et présenter leurs résultats de leurs recherches et projets en lien avec le contexte.Les actes présents contiennent les Brevia et Démonstrations qui ont été retenus après examen par le Comité Scientifique.Nous souhaitons remercier tout particulièrement Isabel Urdapilleta pour sa préparation soigneuse des Actes et Jean-Marc Meunier pour la prise en charge des aspects techniques de la session Démonstrations. Ces actes montre le large spectre de contributions possible à l'étude du contexte, et fournissent des bases indispensables pour une amélioration notable des recherches dans beaucoup de domaines pour les prochaines générations de recherches sur le contexte.